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2018 | OriginalPaper | Chapter

Word Embedding Based on Low-Rank Doubly Stochastic Matrix Decomposition

Authors : Denis Sedov, Zhirong Yang

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

Word embedding, which encodes words into vectors, is an important starting point in natural language processing and commonly used in many text-based machine learning tasks. However, in most current word embedding approaches, the similarity in embedding space is not optimized in the learning. In this paper we propose a novel neighbor embedding method which directly learns an embedding simplex where the similarities between the mapped words are optimal in terms of minimal discrepancy to the input neighborhoods. Our method is built upon two-step random walks between words via topics and thus able to better reveal the topics among the words. Experiment results indicate that our method, compared with another existing word embedding approach, is more favorable for various queries.

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previous chapter Hybridized Character-Word Embedding for Korean Traditional Document Translation

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Title: Word Embedding Based on Low-Rank Doubly Stochastic Matrix Decomposition
Authors: Denis Sedov
Zhirong Yang
Publisher: Springer International Publishing
Book: Neural Information Processing
Print ISBN: 978-3-030-04181-6

Electronic ISBN: 978-3-030-04182-3

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-04182-3_9

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